Gotham City MLXPhysics Workshop

Videos

April 29, 2019

• Thomas Anglès, Roberto Leonarduzi, Stephani Mallat, Louis Thiry, John Sarka, Sixin Zhang: Multiscale Interactions In Physics and Machine Learning With Deep Nets

• Nati Srebro: Can You Help Us Understand Deep Learning

• Short Talks

• Max Welling: Gauge Fields In Deep Learning

April 30, 2019

• Giulio Biroli: From Statistical Physics to Machine Learning and Back

• Bin Yu: Scientific Machine Learning with Empirical Rigor

• Giuseppe Carleo: Deep Learning Many-Body Quantum Physics

• Short Talks/Discussion

• Florent Krzakala: How Does Gradient Descent Find A Good Minima, Instead of Spurious Ones?

• Short Talks

• Eric Vanden-Eijnden: Global Convergence of Neuron Birth-Death Dynamics

• Learning the Universe With Machine Learning Steps to Open the Pandora Box

• Kyunghyum Cho: Can a Neural Net Find a New Linguistic Law On Its Own?

• Barnabas Poczos: Machine Learning for Science Some Open Questions

May 1, 2019

• David Blei: Inferring Stardust (with variational inference)

• Gábor Csányi: Representation and Regression Problems In Molecular Structure and Dynamics

• Gabriel Peyré: Scaling Optimal Transport to High Dimensional Learning

• Davi Geiger: Quantum Interference In Computer Vision

• Bruno Régaldo-Saint Blancard: Statistical Description of the Polarized Interstellar Medium

• Siamak Ravanbakhsh: Representation Learning With Discrete Symmetries

• Erwan Allys: Toward Low-dimensional Representation of Physical Fields

• ML Physics: Examples, Comments, Observations and Questions Part 1

• ML Physics: Examples, Comments, Observations and Questions Part 2

• Concluding Remarks